DCC-DNN: A deep neural network model to predict the drag coefficients of spherical and non-spherical particles aided by empirical correlations Article

Presa-Reyes, Maria, Mahyawansi, Pratik, Hu, Beichao et al. (2024). DCC-DNN: A deep neural network model to predict the drag coefficients of spherical and non-spherical particles aided by empirical correlations . POWDER TECHNOLOGY, 435 10.1016/j.powtec.2024.119388

cited authors

  • Presa-Reyes, Maria; Mahyawansi, Pratik; Hu, Beichao; Mcdaniel, Dwayne; Chen, Shu-Ching

authors

publication date

  • February 15, 2024

published in

keywords

  • BEDS
  • Deep learning
  • Drag coefficient
  • Engineering
  • Engineering, Chemical
  • FLUIDIZATION
  • FORCE
  • Fluid dynamics
  • MACHINE LEARNING APPROACH
  • Meta-learning
  • Non-spherical particles
  • SETTLING VELOCITY
  • SIMULATION
  • Science & Technology
  • Technology

Digital Object Identifier (DOI)

publisher

  • ELSEVIER

volume

  • 435